US2020202382A1PendingUtilityA1

System and process to determine the causal relationship between advertisement delivery data and sales data

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Assignee: Chen yan pingPriority: Dec 19, 2018Filed: Dec 19, 2018Published: Jun 25, 2020
Est. expiryDec 19, 2038(~12.4 yrs left)· nominal 20-yr term from priority
Inventors:Yan Ping ChenJohn Clifton DavisAntonio HudsonAlex Chin
G06Q 30/0243G06N 5/01G06N 7/01G06N 20/20G06Q 30/0201G06N 20/00G06Q 30/0246
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Claims

Abstract

A system and process to determine the causal relationship between advertisement delivery data and sales data are disclosed. According to one embodiment, a method comprises importing advertisement data and sales data. The advertisement data and the sales data are joined to generate a joined data set. Customer journeys are generated for a timeframe from the joined data set. A first group of customers who saw an advertisement of interest are identified. A second group of customers who did not see the advertisement of interest are identified. Each customer of the first group is matched to a customer in the second group who is similar to the customer of the first group. An average treatment effect for the advertisement of interest is calculated.

Claims

exact text as granted — not AI-modified
What is claimed is: 
     
         1 . A method, comprising:
 importing advertisement data and sales data;   joining the advertisement data and the sales data to generate a joined data set;   creating customer journeys for a timeframe from the joined data set;   identifying a first group of customers who saw an advertisement of interest;   identifying a second group of customers who did not see the advertisement of interest;   matching each customer of the first group to a customer in the second group who is similar to the customer of the first group; and   calculating an average treatment effect for the advertisement of interest.   
     
     
         2 . The method of  claim 1 , further comprising generating advertisement performance data from the average treatment effect. 
     
     
         3 . The method of  claim 1 , wherein matching each customer is performed using one or more of propensity matching, inverse probability weighting, optimal matching using linear sum assignment, survival matching and double machine learning. 
     
     
         4 . The method of  claim 1 , wherein matching each customer is performed using one or more of impression counts, time independent; path dependent; and path and time dependent processes. 
     
     
         5 . The method of  claim 1 , further comprising measuring an incremental causal impact of an ad campaign. 
     
     
         6 . The method of  claim 1 , further comprising measuring backlash. 
     
     
         7 . The method of  claim 1 , further comprising measuring diminishing return effects and memory decay effects. 
     
     
         8 . The method of  claim 1 , further comprising tracking one or more of a customer ID and a cookie ID. 
     
     
         9 . The method of  claim 1 , further comprising determining a total number of impressions a user was exposed to in a time frame. 
     
     
         10 . The method of  claim 9 , further comprising determining whether a user converted in the time frame. 
     
     
         11 . The method of  claim 9 , further comprising determining a list of campaign codes that a user was exposed to in the time frame in chronological order. 
     
     
         12 . The method of  claim 9 , further comprising determining timestamps of an user journey in the time frame, a list of timestamps for ad exposures associated with a campaign ID. 
     
     
         13 . The method of  claim 12 , further comprising determining a subset of campaign codes that occurred prior to a conversion. 
     
     
         14 . The method of  claim 1 , further comprising determining a subset of a list of timestamps for ad exposures prior to conversion.

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